With the rapid increase in computer computing power, computer vision, artificial intelligence, machine perception and other fields have also developed rapidly. Image classification, as one of the basic problems in computer vision, has also been developed by leaps and bounds. Image classification is the use of computer intelligence analysis of an image, and then the determination of the category of the image. Conventional image classification algorithms usually rely on RGB images to recognize objects merely. They are easily affected by light changes, object colour changes and background noisy interference. They are not robust in practical application, and their accuracy is very difficult to meet user requirements as well.
The development of depth sensing technology, such as Microsoft's Kinect, can capture the depth picture with high-precision, which is well to make up for the above-mentioned shortcomings of the traditional RGB picture, and which provides possibility for high accuracy object recognition with good robustness. In the field of computer vision and robotics, there are a lot of researches to explore how to effectively use RGB and depth information to improve the accuracy of object recognition. Basically, these algorithms can be summarized as the three major aspects of pattern recognition: pattern (feature) expression, similarity measure and classifier design. Because the present feature expression method is basically independent of the input and cannot adapt to all scales, angles and postures of the object in the input picture automatically, the robustness of object recognition is poor.
In view of the above, the present application has been proposed.